A Bayesian Adaptive Design with Biomarkers for Targeted Therapies

• Activity Number: 332
• Type: Topic Contributed
• Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Jens C. Eickhoff*+ and KyungMann Kim and Jill M. Kolesar and Jason R. Gee
• Companies: Colorado State University and University of Wisconsin-Madison and University of Wisconsin-Madison and Lahey Clinic Medical Center
• Keywords: Bayesian logistic regression ; partial least squares regression ; personalized medicine

Abstract

Targeted therapies are becoming increasingly important for the treatment of various diseases. Biomarkers are a critical component of a targeted therapy as they can be used to identify patients who are more likely to benefit from a treatment. We propose a Bayesian covariate-adjusted response-adaptive (BCARA) randomization design which utilizes individual biomarker profiles and patient's clinical outcomes as they become available during the course of the trial, to assign the most efficacious treatment to individual patients. Predictive biomarker subgroups are determined adaptively using a partial least squares regression approach. A series of simulation studies were conducted to examine the operating characteristics of the proposed study design. We conclude that the proposed design may serve a useful role in the early efficacy phase of targeted therapy development.